Overview

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Dataset statistics

Number of variables17
Number of observations14112
Missing cells15
Missing cells (%)< 0.1%
Total size in memory2.4 MiB
Average record size in memory181.4 B

Variable types

Text17

Reproduction

Analysis started2025-08-12 09:17:55.408768
Analysis finished2025-08-12 09:17:58.490771
Duration3.08 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct2959
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:17:58.888103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length33237
Median length0
Mean length410.5721372
Min length0

Characters and Unicode

Total characters5793994
Distinct characters137
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2575 ?
Unique (%)18.2%

Sample

1st rowYou can obtain overviews for the following object types: Orders Business processes Cost centers Cost objects Materials Work breakdown structures Sales order items Real Estate Objects (RE-FX); The overviews for this object type are only available when the component Flexible Real Estate Management (RE-FX) is activated in the client. You can obtain the following overviews for all of the object types listed above: Overview of non-assigned objects for the relevant object type (such as materials that are not assigned to a profit center) Overview of objects for the relevant object type that are assigned to a specific profit center (such as all cost centers that are assigned to profit center A) or that are assigned to a profit center from a specific profit center group Some object types have special features: For orders , you can analyze the assignments to the following order types: internal order (Controlling), accrual order (Controlling), CO production order, production cost collector, QM order, PP production order, network header, maintenance order, and process order. For Cost Centers , you can additionally display the profit centers to which no cost center has yet been assigned. For Cost Objects, general cost objects are included as well as the cost objects for process manufacturing. For materials, you can additionally navigate to Fast Assignment of Materials , which lets you assign a large number of materials to a unique profit center. For the overviews of sales order items , the following applies: The system checks the read authorization for order types and sales organizations and only displays objects that have the corresponding read authorization. The system proceeds as follows with the selection criteria company code and sales organization : If you do not specify a company code, the system selects the company codes that are assigned to the set controlling area. If you do not specify a sales organization, the system selects the sales organizations that are assigned to the company codes. For the overviews of real estate objects , the following applies: If you do not specify a company code, the system selects the company codes that are assigned to the set controlling area. By double-clicking an object in the assignment overviews, you can navigate directly to the corresponding change transaction for that object. In this way, you can make any missing assignments or correct any incorrect ones.
2nd row
3rd rowThe Asset Accounting component consists of the following parts: Basic functions:Master data (asset maintenance) Basic valuation functions Depreciation Transactions, such as asset acquisitions and retirements Closing operations And more Special valuations: for example, for investment support Preparations for consolidation for group financial statements Information system The basic functions cover the entire life of the asset from the purchase order or initial acquisition (which can be managed as an asset under construction) all the way to the asset retirement. The system calculates, to a large extent automatically, the values for depreciation, interest and other purposes between these two points in time, and places this information at your disposal in varied form using the Information System. There is a report for depreciation forecasting and simulation of the development of asset values. The system enables you to manage values in parallel currencies using different types of valuation. These features simplify the process of preparing for the consolidation of multi-national group concerns. For parallel valuation, you can flexibly assign the depreciation areas of Asset Accounting to the ledgers of the general ledger. The system posts parallel values with the actual values in real time; separate documents are posted for each valuation (that is, each accounting principle). Note The following functions are covered by other components: The Plant Maintenance (PM) component offers functions for the technical management of assets in the form of functional locations and as equipment. The Treasury and Risk Management (TRM) component offers special functions for managing financial assets.
4th row
5th row
ValueCountFrequency (%)
the 73763
 
8.4%
to 21255
 
2.4%
you 19938
 
2.3%
for 19548
 
2.2%
and 17906
 
2.0%
of 16936
 
1.9%
in 16843
 
1.9%
a 16218
 
1.8%
can 12534
 
1.4%
is 9160
 
1.0%
Other values (14302) 654389
74.5%
2025-08-12T11:17:59.551197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
874704
15.1%
e 570550
 
9.8%
t 444135
 
7.7%
a 366045
 
6.3%
n 359622
 
6.2%
o 343880
 
5.9%
i 328731
 
5.7%
s 323650
 
5.6%
r 288715
 
5.0%
c 195905
 
3.4%
Other values (127) 1698057
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5793994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
874704
15.1%
e 570550
 
9.8%
t 444135
 
7.7%
a 366045
 
6.3%
n 359622
 
6.2%
o 343880
 
5.9%
i 328731
 
5.7%
s 323650
 
5.6%
r 288715
 
5.0%
c 195905
 
3.4%
Other values (127) 1698057
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5793994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
874704
15.1%
e 570550
 
9.8%
t 444135
 
7.7%
a 366045
 
6.3%
n 359622
 
6.2%
o 343880
 
5.9%
i 328731
 
5.7%
s 323650
 
5.6%
r 288715
 
5.0%
c 195905
 
3.4%
Other values (127) 1698057
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5793994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
874704
15.1%
e 570550
 
9.8%
t 444135
 
7.7%
a 366045
 
6.3%
n 359622
 
6.2%
o 343880
 
5.9%
i 328731
 
5.7%
s 323650
 
5.6%
r 288715
 
5.0%
c 195905
 
3.4%
Other values (127) 1698057
29.3%
Distinct13903
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:17:59.990608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1464
Median length248
Mean length34.31965703
Min length4

Characters and Unicode

Total characters484319
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13708 ?
Unique (%)97.1%

Sample

1st rowProfit Center Assignment Monitor
2nd rowReverse Journal Entry - Asset Accounting-Specific
3rd rowPost Depreciation Manually - Unplanned and Planned
4th rowPost Retirement (Non-Integrated) - Without Customer
5th rowPost Retirement - By Scrapping
ValueCountFrequency (%)
4935
 
7.2%
display 1911
 
2.8%
for 1345
 
2.0%
create 1340
 
2.0%
manage 1101
 
1.6%
change 920
 
1.4%
maintain 730
 
1.1%
data 662
 
1.0%
of 650
 
1.0%
list 538
 
0.8%
Other values (4422) 53982
79.3%
2025-08-12T11:18:00.636475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54002
 
11.2%
e 46001
 
9.5%
a 33299
 
6.9%
t 33062
 
6.8%
n 31365
 
6.5%
i 29348
 
6.1%
r 26319
 
5.4%
s 25323
 
5.2%
o 25138
 
5.2%
l 16675
 
3.4%
Other values (71) 163787
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 484319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
54002
 
11.2%
e 46001
 
9.5%
a 33299
 
6.9%
t 33062
 
6.8%
n 31365
 
6.5%
i 29348
 
6.1%
r 26319
 
5.4%
s 25323
 
5.2%
o 25138
 
5.2%
l 16675
 
3.4%
Other values (71) 163787
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 484319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
54002
 
11.2%
e 46001
 
9.5%
a 33299
 
6.9%
t 33062
 
6.8%
n 31365
 
6.5%
i 29348
 
6.1%
r 26319
 
5.4%
s 25323
 
5.2%
o 25138
 
5.2%
l 16675
 
3.4%
Other values (71) 163787
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 484319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
54002
 
11.2%
e 46001
 
9.5%
a 33299
 
6.9%
t 33062
 
6.8%
n 31365
 
6.5%
i 29348
 
6.1%
r 26319
 
5.4%
s 25323
 
5.2%
o 25138
 
5.2%
l 16675
 
3.4%
Other values (71) 163787
33.8%
Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:00.847831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length7
Mean length8.523596939
Min length7

Characters and Unicode

Total characters120285
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSAP GUI
2nd rowSAP GUI
3rd rowSAP GUI
4th rowSAP GUI
5th rowSAP GUI
ValueCountFrequency (%)
sap 10346
39.6%
gui 10346
39.6%
transactional 2164
 
8.3%
web 1047
 
4.0%
dynpro 919
 
3.5%
analytical 485
 
1.9%
reuse 195
 
0.7%
component 195
 
0.7%
client 128
 
0.5%
ui 128
 
0.5%
Other values (5) 146
 
0.6%
2025-08-12T11:18:01.218924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11987
10.0%
A 10831
 
9.0%
I 10474
 
8.7%
U 10474
 
8.7%
S 10346
 
8.6%
G 10346
 
8.6%
P 10346
 
8.6%
a 7523
 
6.3%
n 6250
 
5.2%
o 3473
 
2.9%
Other values (23) 28235
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 120285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11987
10.0%
A 10831
 
9.0%
I 10474
 
8.7%
U 10474
 
8.7%
S 10346
 
8.6%
G 10346
 
8.6%
P 10346
 
8.6%
a 7523
 
6.3%
n 6250
 
5.2%
o 3473
 
2.9%
Other values (23) 28235
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 120285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11987
10.0%
A 10831
 
9.0%
I 10474
 
8.7%
U 10474
 
8.7%
S 10346
 
8.6%
G 10346
 
8.6%
P 10346
 
8.6%
a 7523
 
6.3%
n 6250
 
5.2%
o 3473
 
2.9%
Other values (23) 28235
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 120285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11987
10.0%
A 10831
 
9.0%
I 10474
 
8.7%
U 10474
 
8.7%
S 10346
 
8.6%
G 10346
 
8.6%
P 10346
 
8.6%
a 7523
 
6.3%
n 6250
 
5.2%
o 3473
 
2.9%
Other values (23) 28235
23.5%
Distinct1126
Distinct (%)8.0%
Missing15
Missing (%)0.1%
Memory size736.5 KiB
2025-08-12T11:18:01.558710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length60
Median length46
Mean length23.64942896
Min length0

Characters and Unicode

Total characters333386
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique268 ?
Unique (%)1.9%

Sample

1st rowProfit Center Accounting
2nd rowBasic Functions
3rd rowAsset Accounting
4th rowBasic Functions
5th rowBasic Functions
ValueCountFrequency (%)
and 2663
 
6.3%
management 2100
 
5.0%
accounting 1534
 
3.6%
production 1136
 
2.7%
for 1070
 
2.5%
contract 1045
 
2.5%
revenue 922
 
2.2%
accounts 715
 
1.7%
fiori 699
 
1.7%
receivable 690
 
1.6%
Other values (1056) 29471
70.1%
2025-08-12T11:18:02.205821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 33187
 
10.0%
e 28946
 
8.7%
27966
 
8.4%
a 25555
 
7.7%
t 23839
 
7.2%
i 22002
 
6.6%
o 20528
 
6.2%
r 16100
 
4.8%
c 15368
 
4.6%
s 12081
 
3.6%
Other values (58) 107814
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 33187
 
10.0%
e 28946
 
8.7%
27966
 
8.4%
a 25555
 
7.7%
t 23839
 
7.2%
i 22002
 
6.6%
o 20528
 
6.2%
r 16100
 
4.8%
c 15368
 
4.6%
s 12081
 
3.6%
Other values (58) 107814
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 33187
 
10.0%
e 28946
 
8.7%
27966
 
8.4%
a 25555
 
7.7%
t 23839
 
7.2%
i 22002
 
6.6%
o 20528
 
6.2%
r 16100
 
4.8%
c 15368
 
4.6%
s 12081
 
3.6%
Other values (58) 107814
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 33187
 
10.0%
e 28946
 
8.7%
27966
 
8.4%
a 25555
 
7.7%
t 23839
 
7.2%
i 22002
 
6.6%
o 20528
 
6.2%
r 16100
 
4.8%
c 15368
 
4.6%
s 12081
 
3.6%
Other values (58) 107814
32.3%
Distinct1121
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:02.528497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3668
Median length2023
Mean length34.85352891
Min length0

Characters and Unicode

Total characters491853
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique468 ?
Unique (%)3.3%

Sample

1st rowDivisional Accountant
2nd rowAsset Accountant
3rd rowAsset Accountant
4th rowAsset Accountant
5th rowAsset Accountant
ValueCountFrequency (%)
5502
 
9.0%
specialist 2945
 
4.8%
accountant 2898
 
4.7%
manager 2875
 
4.7%
for 1760
 
2.9%
accounts 1741
 
2.8%
iog 1507
 
2.5%
data 1443
 
2.4%
payable 1350
 
2.2%
receivable 1213
 
2.0%
Other values (330) 37880
62.0%
2025-08-12T11:18:03.608707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52460
 
10.7%
e 45243
 
9.2%
a 41216
 
8.4%
n 37018
 
7.5%
t 32860
 
6.7%
r 29183
 
5.9%
i 26058
 
5.3%
c 23051
 
4.7%
o 21856
 
4.4%
l 17159
 
3.5%
Other values (45) 165749
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 491853
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
52460
 
10.7%
e 45243
 
9.2%
a 41216
 
8.4%
n 37018
 
7.5%
t 32860
 
6.7%
r 29183
 
5.9%
i 26058
 
5.3%
c 23051
 
4.7%
o 21856
 
4.4%
l 17159
 
3.5%
Other values (45) 165749
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 491853
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
52460
 
10.7%
e 45243
 
9.2%
a 41216
 
8.4%
n 37018
 
7.5%
t 32860
 
6.7%
r 29183
 
5.9%
i 26058
 
5.3%
c 23051
 
4.7%
o 21856
 
4.4%
l 17159
 
3.5%
Other values (45) 165749
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 491853
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
52460
 
10.7%
e 45243
 
9.2%
a 41216
 
8.4%
n 37018
 
7.5%
t 32860
 
6.7%
r 29183
 
5.9%
i 26058
 
5.3%
c 23051
 
4.7%
o 21856
 
4.4%
l 17159
 
3.5%
Other values (45) 165749
33.7%
Distinct12636
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:04.026990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3587
Median length315
Mean length32.98944161
Min length0

Characters and Unicode

Total characters465547
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12435 ?
Unique (%)88.1%

Sample

1st rowProfit Center Assignment Monitor
2nd rowReverse Journal Entry - Asset Accounting-Specific
3rd rowPost Depreciation Manually - Unplanned and Planned
4th rowPost Retirement (Non-Integrated) - Without Customer
5th rowPost Retirement - By Scrapping
ValueCountFrequency (%)
5461
 
8.3%
display 1808
 
2.7%
create 1289
 
2.0%
for 1227
 
1.9%
manage 1118
 
1.7%
change 864
 
1.3%
maintain 725
 
1.1%
data 644
 
1.0%
of 607
 
0.9%
list 530
 
0.8%
Other values (4199) 51507
78.3%
2025-08-12T11:18:04.707444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52914
 
11.4%
e 44250
 
9.5%
a 32302
 
6.9%
t 31826
 
6.8%
n 30176
 
6.5%
i 27839
 
6.0%
r 25018
 
5.4%
s 24410
 
5.2%
o 23851
 
5.1%
l 16202
 
3.5%
Other values (72) 156759
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 465547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
52914
 
11.4%
e 44250
 
9.5%
a 32302
 
6.9%
t 31826
 
6.8%
n 30176
 
6.5%
i 27839
 
6.0%
r 25018
 
5.4%
s 24410
 
5.2%
o 23851
 
5.1%
l 16202
 
3.5%
Other values (72) 156759
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 465547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
52914
 
11.4%
e 44250
 
9.5%
a 32302
 
6.9%
t 31826
 
6.8%
n 30176
 
6.5%
i 27839
 
6.0%
r 25018
 
5.4%
s 24410
 
5.2%
o 23851
 
5.1%
l 16202
 
3.5%
Other values (72) 156759
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 465547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
52914
 
11.4%
e 44250
 
9.5%
a 32302
 
6.9%
t 31826
 
6.8%
n 30176
 
6.5%
i 27839
 
6.0%
r 25018
 
5.4%
s 24410
 
5.2%
o 23851
 
5.1%
l 16202
 
3.5%
Other values (72) 156759
33.7%
Distinct4024
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:05.259412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6352
Median length410
Mean length430.4900085
Min length0

Characters and Unicode

Total characters6075075
Distinct characters102
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3459 ?
Unique (%)24.5%

Sample

1st rowThe assignment monitor provides you with an overview of all the assignments that you have made from various objects to profit centers and supports you when you make or change assignments. Recommendation Incorrect assignments lead to incorrect transaction data in Profit Center Accounting, which can usually only be corrected with significant effort. We therefore recommend that you check the assignments carefully.
2nd rowThis app is a SAP GUI for HTML transaction. These classic transactions are available in the SAP Fiori theme to support a seamless user experience across the SAP Fiori launchpad and to provide a harmonized user experience across on-premise and cloud solutions. The single point of entry for SAP Fiori apps and classic applications in SAP S/4HANA Cloud Private Edition and SAP S/4HANA is the SAP Fiori launchpad.
3rd rowAsset Accounting in the SAP system is used for managing and monitoring fixed assets. In Financial Accounting, it serves as a subsidiary ledger to the general ledger, providing detailed information on transactions involving fixed assets.
4th rowThis app is a SAP GUI for HTML transaction. These classic transactions are available in the SAP Fiori theme to support a seamless user experience across the SAP Fiori launchpad and to provide a harmonized user experience across on-premise and cloud solutions. The single point of entry for SAP Fiori apps and classic applications in SAP S/4HANA Cloud Private Edition and SAP S/4HANA is the SAP Fiori launchpad.
5th rowThis app is a SAP GUI for HTML transaction. These classic transactions are available in the SAP Fiori theme to support a seamless user experience across the SAP Fiori launchpad and to provide a harmonized user experience across on-premise and cloud solutions. The single point of entry for SAP Fiori apps and classic applications in SAP S/4HANA Cloud Private Edition and SAP S/4HANA is the SAP Fiori launchpad.
ValueCountFrequency (%)
the 62108
 
6.3%
sap 60133
 
6.1%
and 43600
 
4.4%
fiori 34532
 
3.5%
a 34115
 
3.5%
to 28239
 
2.9%
for 26038
 
2.6%
in 24890
 
2.5%
is 21034
 
2.1%
user 17425
 
1.8%
Other values (9062) 630686
64.2%
2025-08-12T11:18:06.072575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
968998
16.0%
e 496938
 
8.2%
a 429629
 
7.1%
i 414031
 
6.8%
s 374447
 
6.2%
o 362744
 
6.0%
n 346725
 
5.7%
t 336746
 
5.5%
r 310816
 
5.1%
c 215105
 
3.5%
Other values (92) 1818896
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6075075
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
968998
16.0%
e 496938
 
8.2%
a 429629
 
7.1%
i 414031
 
6.8%
s 374447
 
6.2%
o 362744
 
6.0%
n 346725
 
5.7%
t 336746
 
5.5%
r 310816
 
5.1%
c 215105
 
3.5%
Other values (92) 1818896
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6075075
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
968998
16.0%
e 496938
 
8.2%
a 429629
 
7.1%
i 414031
 
6.8%
s 374447
 
6.2%
o 362744
 
6.0%
n 346725
 
5.7%
t 336746
 
5.5%
r 310816
 
5.1%
c 215105
 
3.5%
Other values (92) 1818896
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6075075
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
968998
16.0%
e 496938
 
8.2%
a 429629
 
7.1%
i 414031
 
6.8%
s 374447
 
6.2%
o 362744
 
6.0%
n 346725
 
5.7%
t 336746
 
5.5%
r 310816
 
5.1%
c 215105
 
3.5%
Other values (92) 1818896
29.9%
Distinct2225
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:06.553942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length100
Median length88
Mean length77.70011338
Min length0

Characters and Unicode

Total characters1096504
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2083 ?
Unique (%)14.8%

Sample

1st rowSAP Fiori theme for SAP GUI for HTML (SAP S/4HANA Cloud Private Edition and SAP S/4HANA)
2nd rowSAP Fiori theme for SAP GUI for HTML (SAP S/4HANA Cloud Private Edition and SAP S/4HANA)
3rd rowSAP Fiori theme for SAP GUI for HTML (SAP S/4HANA Cloud Private Edition and SAP S/4HANA)
4th rowSAP Fiori theme for SAP GUI for HTML (SAP S/4HANA Cloud Private Edition and SAP S/4HANA)
5th rowSAP Fiori theme for SAP GUI for HTML (SAP S/4HANA Cloud Private Edition and SAP S/4HANA)
ValueCountFrequency (%)
sap 44718
23.0%
s/4hana 23099
11.9%
for 22658
11.7%
fiori 11399
 
5.9%
edition 11386
 
5.9%
and 11341
 
5.8%
cloud 11289
 
5.8%
private 11215
 
5.8%
theme 11214
 
5.8%
gui 10346
 
5.3%
Other values (1853) 25792
13.3%
2025-08-12T11:18:07.243592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
180754
16.5%
A 93599
 
8.5%
S 69088
 
6.3%
o 62072
 
5.7%
i 61859
 
5.6%
P 58118
 
5.3%
r 50231
 
4.6%
e 43305
 
3.9%
t 39560
 
3.6%
d 35631
 
3.2%
Other values (64) 402287
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1096504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
180754
16.5%
A 93599
 
8.5%
S 69088
 
6.3%
o 62072
 
5.7%
i 61859
 
5.6%
P 58118
 
5.3%
r 50231
 
4.6%
e 43305
 
3.9%
t 39560
 
3.6%
d 35631
 
3.2%
Other values (64) 402287
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1096504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
180754
16.5%
A 93599
 
8.5%
S 69088
 
6.3%
o 62072
 
5.7%
i 61859
 
5.6%
P 58118
 
5.3%
r 50231
 
4.6%
e 43305
 
3.9%
t 39560
 
3.6%
d 35631
 
3.2%
Other values (64) 402287
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1096504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
180754
16.5%
A 93599
 
8.5%
S 69088
 
6.3%
o 62072
 
5.7%
i 61859
 
5.6%
P 58118
 
5.3%
r 50231
 
4.6%
e 43305
 
3.9%
t 39560
 
3.6%
d 35631
 
3.2%
Other values (64) 402287
36.7%
Distinct1087
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:07.710582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length243
Median length100
Mean length90.83071145
Min length0

Characters and Unicode

Total characters1281803
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique688 ?
Unique (%)4.9%

Sample

1st rowSAP Fiori visual theme for classic applications in SAP S/4HANA Cloud Private Edition and SAP S/4HANA
2nd rowSAP Fiori visual theme for classic applications in SAP S/4HANA Cloud Private Edition and SAP S/4HANA
3rd rowSAP Fiori visual theme for classic applications in SAP S/4HANA Cloud Private Edition and SAP S/4HANA
4th rowSAP Fiori visual theme for classic applications in SAP S/4HANA Cloud Private Edition and SAP S/4HANA
5th rowSAP Fiori visual theme for classic applications in SAP S/4HANA Cloud Private Edition and SAP S/4HANA
ValueCountFrequency (%)
sap 35102
17.4%
s/4hana 23104
11.4%
for 12470
 
6.2%
and 12122
 
6.0%
in 12052
 
6.0%
fiori 11940
 
5.9%
edition 11351
 
5.6%
cloud 11316
 
5.6%
private 11238
 
5.6%
classic 11214
 
5.6%
Other values (1664) 49975
24.8%
2025-08-12T11:18:08.402214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
188255
 
14.7%
i 123412
 
9.6%
A 81782
 
6.4%
a 78490
 
6.1%
o 65932
 
5.1%
S 58548
 
4.6%
n 57062
 
4.5%
t 54172
 
4.2%
s 52880
 
4.1%
l 49334
 
3.8%
Other values (63) 471936
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1281803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
188255
 
14.7%
i 123412
 
9.6%
A 81782
 
6.4%
a 78490
 
6.1%
o 65932
 
5.1%
S 58548
 
4.6%
n 57062
 
4.5%
t 54172
 
4.2%
s 52880
 
4.1%
l 49334
 
3.8%
Other values (63) 471936
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1281803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
188255
 
14.7%
i 123412
 
9.6%
A 81782
 
6.4%
a 78490
 
6.1%
o 65932
 
5.1%
S 58548
 
4.6%
n 57062
 
4.5%
t 54172
 
4.2%
s 52880
 
4.1%
l 49334
 
3.8%
Other values (63) 471936
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1281803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
188255
 
14.7%
i 123412
 
9.6%
A 81782
 
6.4%
a 78490
 
6.1%
o 65932
 
5.1%
S 58548
 
4.6%
n 57062
 
4.5%
t 54172
 
4.2%
s 52880
 
4.1%
l 49334
 
3.8%
Other values (63) 471936
36.8%
Distinct131
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:08.772340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length408
Median length0
Mean length15.21152211
Min length0

Characters and Unicode

Total characters214665
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.2%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
and 3728
 
14.8%
finance 2677
 
10.6%
operations 1178
 
4.7%
management 1163
 
4.6%
sales 1060
 
4.2%
budget 936
 
3.7%
development 728
 
2.9%
product 711
 
2.8%
corporate 700
 
2.8%
chain 695
 
2.8%
Other values (82) 11626
46.1%
2025-08-12T11:18:09.390494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 23271
 
10.8%
23105
 
10.8%
e 20342
 
9.5%
a 17827
 
8.3%
i 13862
 
6.5%
t 11947
 
5.6%
r 10684
 
5.0%
, 10076
 
4.7%
o 7546
 
3.5%
c 7410
 
3.5%
Other values (38) 68595
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 214665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 23271
 
10.8%
23105
 
10.8%
e 20342
 
9.5%
a 17827
 
8.3%
i 13862
 
6.5%
t 11947
 
5.6%
r 10684
 
5.0%
, 10076
 
4.7%
o 7546
 
3.5%
c 7410
 
3.5%
Other values (38) 68595
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 214665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 23271
 
10.8%
23105
 
10.8%
e 20342
 
9.5%
a 17827
 
8.3%
i 13862
 
6.5%
t 11947
 
5.6%
r 10684
 
5.0%
, 10076
 
4.7%
o 7546
 
3.5%
c 7410
 
3.5%
Other values (38) 68595
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 214665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 23271
 
10.8%
23105
 
10.8%
e 20342
 
9.5%
a 17827
 
8.3%
i 13862
 
6.5%
t 11947
 
5.6%
r 10684
 
5.0%
, 10076
 
4.7%
o 7546
 
3.5%
c 7410
 
3.5%
Other values (38) 68595
32.0%
Distinct241
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:09.765330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1592
Median length0
Mean length0.82263322
Min length0

Characters and Unicode

Total characters11609
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)1.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
616
34.7%
deprecated 130
 
7.3%
reports,pending 93
 
5.2%
contracts 46
 
2.6%
open 25
 
1.4%
archive 23
 
1.3%
processing 22
 
1.2%
mass 21
 
1.2%
active 20
 
1.1%
for 19
 
1.1%
Other values (298) 761
42.8%
2025-08-12T11:18:10.438354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1377
 
11.9%
, 1315
 
11.3%
t 820
 
7.1%
r 747
 
6.4%
s 697
 
6.0%
n 666
 
5.7%
600
 
5.2%
o 549
 
4.7%
a 539
 
4.6%
i 512
 
4.4%
Other values (54) 3787
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1377
 
11.9%
, 1315
 
11.3%
t 820
 
7.1%
r 747
 
6.4%
s 697
 
6.0%
n 666
 
5.7%
600
 
5.2%
o 549
 
4.7%
a 539
 
4.6%
i 512
 
4.4%
Other values (54) 3787
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1377
 
11.9%
, 1315
 
11.3%
t 820
 
7.1%
r 747
 
6.4%
s 697
 
6.0%
n 666
 
5.7%
600
 
5.2%
o 549
 
4.7%
a 539
 
4.6%
i 512
 
4.4%
Other values (54) 3787
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1377
 
11.9%
, 1315
 
11.3%
t 820
 
7.1%
r 747
 
6.4%
s 697
 
6.0%
n 666
 
5.7%
600
 
5.2%
o 549
 
4.7%
a 539
 
4.6%
i 512
 
4.4%
Other values (54) 3787
32.6%
Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:10.669658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length80
Median length0
Mean length2.238803855
Min length0

Characters and Unicode

Total characters31594
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
cross 1930
42.3%
industry 1930
42.3%
and 114
 
2.5%
oil 71
 
1.6%
gas 71
 
1.6%
energy 71
 
1.6%
retail 66
 
1.4%
public 51
 
1.1%
sector 51
 
1.1%
defense 33
 
0.7%
Other values (15) 175
 
3.8%
2025-08-12T11:18:11.053865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 6060
19.2%
r 4132
13.1%
2347
 
7.4%
n 2252
 
7.1%
t 2148
 
6.8%
u 2104
 
6.7%
d 2077
 
6.6%
o 2060
 
6.5%
y 2029
 
6.4%
C 1957
 
6.2%
Other values (27) 4428
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 6060
19.2%
r 4132
13.1%
2347
 
7.4%
n 2252
 
7.1%
t 2148
 
6.8%
u 2104
 
6.7%
d 2077
 
6.6%
o 2060
 
6.5%
y 2029
 
6.4%
C 1957
 
6.2%
Other values (27) 4428
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 6060
19.2%
r 4132
13.1%
2347
 
7.4%
n 2252
 
7.1%
t 2148
 
6.8%
u 2104
 
6.7%
d 2077
 
6.6%
o 2060
 
6.5%
y 2029
 
6.4%
C 1957
 
6.2%
Other values (27) 4428
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 6060
19.2%
r 4132
13.1%
2347
 
7.4%
n 2252
 
7.1%
t 2148
 
6.8%
u 2104
 
6.7%
d 2077
 
6.6%
o 2060
 
6.5%
y 2029
 
6.4%
C 1957
 
6.2%
Other values (27) 4428
14.0%
Distinct100
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:11.332340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length742
Median length0
Mean length76.12011054
Min length0

Characters and Unicode

Total characters1074207
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
and 16970
 
13.9%
high 5333
 
4.4%
tech 5333
 
4.4%
travel 5221
 
4.3%
transportation 5127
 
4.2%
products 3719
 
3.0%
defense 3300
 
2.7%
entertainment 3079
 
2.5%
oil 1984
 
1.6%
energy 1984
 
1.6%
Other values (45) 70438
57.5%
2025-08-12T11:18:11.841934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
120351
 
11.2%
e 88040
 
8.2%
n 81949
 
7.6%
i 74816
 
7.0%
a 65895
 
6.1%
r 63997
 
6.0%
t 62620
 
5.8%
s 56348
 
5.2%
o 55147
 
5.1%
; 47748
 
4.4%
Other values (35) 357296
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1074207
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
120351
 
11.2%
e 88040
 
8.2%
n 81949
 
7.6%
i 74816
 
7.0%
a 65895
 
6.1%
r 63997
 
6.0%
t 62620
 
5.8%
s 56348
 
5.2%
o 55147
 
5.1%
; 47748
 
4.4%
Other values (35) 357296
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1074207
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
120351
 
11.2%
e 88040
 
8.2%
n 81949
 
7.6%
i 74816
 
7.0%
a 65895
 
6.1%
r 63997
 
6.0%
t 62620
 
5.8%
s 56348
 
5.2%
o 55147
 
5.1%
; 47748
 
4.4%
Other values (35) 357296
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1074207
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
120351
 
11.2%
e 88040
 
8.2%
n 81949
 
7.6%
i 74816
 
7.0%
a 65895
 
6.1%
r 63997
 
6.0%
t 62620
 
5.8%
s 56348
 
5.2%
o 55147
 
5.1%
; 47748
 
4.4%
Other values (35) 357296
33.3%
Distinct2251
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:12.263032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28449
Median length9675
Mean length299.3521825
Min length7

Characters and Unicode

Total characters4224458
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1454 ?
Unique (%)10.3%

Sample

1st rowDivisional Accountant : Defines division-related master data and transfer prices between different divisions. Analyzes costs and margin for division. Prepares the division reporting package. Cost Accountant - Overhead : Defines overhead master data and the period-end allocation cycle. Implements period-end allocation and monitors overhead costs. Reports expenses on the income statement in the period in which they are incurred. Controller : Sets up and edits organizational structures so that all processes and reports are as accurate as possible. Edits master data, reorganizes company structures, and performs period-end functions. General Ledger Accountant : Carries out daily activities in general ledger, for example by recording transactions and making adjustment postings, and prepares financial statements.
2nd rowAsset Accountant : Capitalizes costs of assets during purchasing and production processes, defines depreciation parameters, and records depreciation. Ensures correctness, completeness, and documentation of the balance sheet for fixed assets.
3rd rowAsset Accountant : Capitalizes costs of assets during purchasing and production processes, defines depreciation parameters, and records depreciation. Ensures correctness, completeness, and documentation of the balance sheet for fixed assets.
4th rowAsset Accountant : Capitalizes costs of assets during purchasing and production processes, defines depreciation parameters, and records depreciation. Ensures correctness, completeness, and documentation of the balance sheet for fixed assets.
5th rowAsset Accountant : Capitalizes costs of assets during purchasing and production processes, defines depreciation parameters, and records depreciation. Ensures correctness, completeness, and documentation of the balance sheet for fixed assets.
ValueCountFrequency (%)
and 35097
 
5.9%
29373
 
5.0%
the 22667
 
3.8%
of 13035
 
2.2%
for 12609
 
2.1%
to 9872
 
1.7%
accounts 8536
 
1.4%
is 8260
 
1.4%
in 8062
 
1.4%
accountant 7631
 
1.3%
Other values (1737) 437603
73.8%
2025-08-12T11:18:12.952541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
578633
13.7%
e 372929
 
8.8%
n 326980
 
7.7%
a 313061
 
7.4%
t 282826
 
6.7%
s 263224
 
6.2%
i 252908
 
6.0%
o 248353
 
5.9%
r 234484
 
5.6%
c 178996
 
4.2%
Other values (59) 1172064
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4224458
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
578633
13.7%
e 372929
 
8.8%
n 326980
 
7.7%
a 313061
 
7.4%
t 282826
 
6.7%
s 263224
 
6.2%
i 252908
 
6.0%
o 248353
 
5.9%
r 234484
 
5.6%
c 178996
 
4.2%
Other values (59) 1172064
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4224458
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
578633
13.7%
e 372929
 
8.8%
n 326980
 
7.7%
a 313061
 
7.4%
t 282826
 
6.7%
s 263224
 
6.2%
i 252908
 
6.0%
o 248353
 
5.9%
r 234484
 
5.6%
c 178996
 
4.2%
Other values (59) 1172064
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4224458
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
578633
13.7%
e 372929
 
8.8%
n 326980
 
7.7%
a 313061
 
7.4%
t 282826
 
6.7%
s 263224
 
6.2%
i 252908
 
6.0%
o 248353
 
5.9%
r 234484
 
5.6%
c 178996
 
4.2%
Other values (59) 1172064
27.7%
Distinct353
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:13.350782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length282
Median length0
Mean length8.603316327
Min length0

Characters and Unicode

Total characters121410
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)0.7%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
s/4 2407
 
16.8%
management 1376
 
9.6%
and 892
 
6.2%
cld 429
 
3.0%
data 253
 
1.8%
accounting 217
 
1.5%
processing 192
 
1.3%
for 179
 
1.2%
sales 166
 
1.2%
contract 164
 
1.1%
Other values (408) 8087
56.3%
2025-08-12T11:18:14.281500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12146
 
10.0%
n 10196
 
8.4%
e 9998
 
8.2%
a 8996
 
7.4%
t 7249
 
6.0%
i 5723
 
4.7%
S 4584
 
3.8%
o 4562
 
3.8%
r 4099
 
3.4%
( 3664
 
3.0%
Other values (49) 50193
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12146
 
10.0%
n 10196
 
8.4%
e 9998
 
8.2%
a 8996
 
7.4%
t 7249
 
6.0%
i 5723
 
4.7%
S 4584
 
3.8%
o 4562
 
3.8%
r 4099
 
3.4%
( 3664
 
3.0%
Other values (49) 50193
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12146
 
10.0%
n 10196
 
8.4%
e 9998
 
8.2%
a 8996
 
7.4%
t 7249
 
6.0%
i 5723
 
4.7%
S 4584
 
3.8%
o 4562
 
3.8%
r 4099
 
3.4%
( 3664
 
3.0%
Other values (49) 50193
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12146
 
10.0%
n 10196
 
8.4%
e 9998
 
8.2%
a 8996
 
7.4%
t 7249
 
6.0%
i 5723
 
4.7%
S 4584
 
3.8%
o 4562
 
3.8%
r 4099
 
3.4%
( 3664
 
3.0%
Other values (49) 50193
41.3%
Distinct1498
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:14.593358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2204
Median length0
Mean length5.115150227
Min length0

Characters and Unicode

Total characters72185
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1032 ?
Unique (%)7.3%

Sample

1st row
2nd rowAsset Accounting-Specific
3rd rowUnplanned and Planned
4th rowWithout Customer
5th rowBy Scrapping
ValueCountFrequency (%)
427
 
4.6%
for 268
 
2.9%
russia 152
 
1.6%
management 126
 
1.4%
system 123
 
1.3%
information 113
 
1.2%
china 109
 
1.2%
analysis 93
 
1.0%
orders 90
 
1.0%
greece 88
 
0.9%
Other values (1444) 7730
82.9%
2025-08-12T11:18:15.114624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6366
 
8.8%
a 5398
 
7.5%
n 5054
 
7.0%
t 4715
 
6.5%
i 4706
 
6.5%
r 4538
 
6.3%
4500
 
6.2%
s 3993
 
5.5%
o 3868
 
5.4%
l 2784
 
3.9%
Other values (65) 26263
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6366
 
8.8%
a 5398
 
7.5%
n 5054
 
7.0%
t 4715
 
6.5%
i 4706
 
6.5%
r 4538
 
6.3%
4500
 
6.2%
s 3993
 
5.5%
o 3868
 
5.4%
l 2784
 
3.9%
Other values (65) 26263
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6366
 
8.8%
a 5398
 
7.5%
n 5054
 
7.0%
t 4715
 
6.5%
i 4706
 
6.5%
r 4538
 
6.3%
4500
 
6.2%
s 3993
 
5.5%
o 3868
 
5.4%
l 2784
 
3.9%
Other values (65) 26263
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6366
 
8.8%
a 5398
 
7.5%
n 5054
 
7.0%
t 4715
 
6.5%
i 4706
 
6.5%
r 4538
 
6.3%
4500
 
6.2%
s 3993
 
5.5%
o 3868
 
5.4%
l 2784
 
3.9%
Other values (65) 26263
36.4%

Title
Text

Distinct11637
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Memory size736.5 KiB
2025-08-12T11:18:15.566300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2343
Median length257
Mean length26.94132653
Min length0

Characters and Unicode

Total characters380196
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10871 ?
Unique (%)77.0%

Sample

1st rowProfit Center Assignment Monitor
2nd rowReverse Journal Entry
3rd rowPost Depreciation Manually
4th rowPost Retirement (Non-Integrated)
5th rowPost Retirement
ValueCountFrequency (%)
display 1603
 
3.2%
create 1161
 
2.3%
manage 954
 
1.9%
for 947
 
1.9%
change 821
 
1.6%
maintain 686
 
1.4%
of 568
 
1.1%
data 549
 
1.1%
list 429
 
0.9%
documents 350
 
0.7%
Other values (4569) 42030
83.9%
2025-08-12T11:18:16.202023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 38018
 
10.0%
37232
 
9.8%
t 27222
 
7.2%
a 27015
 
7.1%
n 25292
 
6.7%
i 23285
 
6.1%
r 20573
 
5.4%
s 20487
 
5.4%
o 20103
 
5.3%
l 13481
 
3.5%
Other values (72) 127488
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 380196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 38018
 
10.0%
37232
 
9.8%
t 27222
 
7.2%
a 27015
 
7.1%
n 25292
 
6.7%
i 23285
 
6.1%
r 20573
 
5.4%
s 20487
 
5.4%
o 20103
 
5.3%
l 13481
 
3.5%
Other values (72) 127488
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 380196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 38018
 
10.0%
37232
 
9.8%
t 27222
 
7.2%
a 27015
 
7.1%
n 25292
 
6.7%
i 23285
 
6.1%
r 20573
 
5.4%
s 20487
 
5.4%
o 20103
 
5.3%
l 13481
 
3.5%
Other values (72) 127488
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 380196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 38018
 
10.0%
37232
 
9.8%
t 27222
 
7.2%
a 27015
 
7.1%
n 25292
 
6.7%
i 23285
 
6.1%
r 20573
 
5.4%
s 20487
 
5.4%
o 20103
 
5.3%
l 13481
 
3.5%
Other values (72) 127488
33.5%